Ultrasound-assisted adsorption of Methylene Blue dye on to the copper-doped zinc sulfide nanoparticles loaded on activated carbon: Optimization by artificial neural network and genetic algorithm

In this study, artificial neural network (ANN) and genetic algorithm (GA) were applied to find the optimal practical conditions, i.e., adsorbent dosage, sonication time, MB concentration, AO concentration, Er concentration as input parameters and % removal Methylene Blue (MB) as output parameters. To enhance the adsorption of MB dye from ternary aqueous solutions including Auramine-O (AO), Erythrosine (Er) and MB were quickly carried out onto the copper-doped zinc sulfide nanoparticles loaded on activated carbon (ZnS:Cu-NP-AC). After examination the different ANN architectures the mean absolute error is obtained with 1.5% and R2 = 0.9847. The proposed structure was used as fitting function for genetic algorithm. Comparison of the predicted values with the experimental data revealed that the GA–ANN model is a powerful method to find the optimal conditions for preparing of copper-doped zinc sulfide nanoparticles loaded on activated carbon with the highest adsorption of MB dye. Moreover, sensitivity analysis showed that MB concentration and Er concentration and Er concentration have the higher and lower effect on % removal MB, respectively.